Comments (2)
Hello @morenoh149, thanks for your interest and ideas!
As for preptrained backbones, this is really a key feature of this repository, so a lot of attention is paid to it. However, I absolutely agree that the description of the main architectures is also very important to add.
It would be great if you could help make this readme more clear and describe the ideas you listed. Thank you!
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@qubvel lets start by having you answer each of my questions, or providing reading material that answers my questions.
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Related Issues (20)
- module 'segmentation_models.losses' has no attribute 'DiceLoss' HOT 1
- Call arguments received by layer 'model_18' (type Functional): • inputs=tf.Tensor(shape=(1, 384, 8), dtype=float32) • training=True • mask=None
- Any way to get per class IoU scores?
- How to get PA value and mPA value? HOT 1
- How to extract saliency map from the ImageNet pretrained with non-RBG input?
- How to implement more metrics in model.compile? HOT 2
- Dropout layers in U-net and Linknet
- ModuleNotFoundError: No module named 'keras.legacy_tf_layers' issue
- Kindly add the Segformer Backbone! :) HOT 1
- module 'keras.utils.generic_utils' has no attribute 'get_custom_objects' HOT 4
- Good IOU Score on training data, but bad segmentation on testing data. HOT 2
- batch size when predicting HOT 1
- Segformer/Transformer Backbone
- File "/usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/interpreter.py", line 915, in invoke self._interpreter.Invoke() RuntimeError: tensorflow/lite/kernels/concatenation.cc:158 t->dims->data[d] != t0->dims->data[d] (1 != 2)Node number 304 (CONCATENATION) failed to prepare.
- Is there option to add classification head after encoder like in pytorch version?
- AttributeError: module 'keras.utils' has no attribute 'generic_utils'
- Incorporating sample weights in loss function
- Understanding difference between TensorFlow and PyTorch implementations of Unet
- Equation .. math:: is misleading
- How to apply inferred mask to image
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